Image registration refers to the process of coordinating two or more data sets representing images of the same scene or object at different times, different perspective, and/or by different sensing devices. Image registration is an important step for analyzing images, in which plurality of data obtained from different measurements exist. Medical image registration often involve dealing with non-rigid or elastic registration to adapt to deformation of the imaged subject, for example, due to breathing, blood flows, and other anatomical changes, etc.
Existing medical image registration methodologies of two dimensional (2D) image sequences utilize both linear and non-linear techniques. Those techniques have used landmarks, correlation, and mutual information to perform registration of general medical images. However, many sequences of medical images have low contrast features such as open surgical images showing organs and other tissue. The borders of the organs are not generally sharp and are difficult to locate using standard image processing techniques.
Researchers at the National Institutes of Health (NIH) have recorded 2D real-time infrared and visible optical images during exploratory surgery for both humans and animals. Those images record, for example, kidneys, livers, and other specific regions in the body for later thermal analysis of physiological changes. A major problem for the analysis is that movement occurs during the surgery due to respiration, blood flow, or mechanical motion from the surgical procedure. Those effects cause unacceptable alignment through the image sequence, making local temperature measurements impossible. The soft organs and tissues are not rigid bodies but have deformable surfaces. In addition, the organs themselves are 3D volumes with 3D motions. While medical image registration has been researched, the application of deformable registration to 2D time image sequences with non-rigid 3D structures has not been extensively studied.
In another aspect, intraoperative optical images of exposed organs in visible, near-infrared, and infrared (IR) wavelengths in the body have the potential to be useful for real-time, non-invasive assessment of organ viability and image guidance during surgical intervention. Relatively low cost, non-invasiveness and good specificity for physiological changes make optical imaging desirable for a variety of surgical procedures.
However, as mentioned above, the motion of the internal organs presents significant challenge for real-time data analysis of recorded two-dimensional video sequences. The movement of the kidney, liver, urethra, etc. observed during surgery due to respiration, cardiac motion, and blood flow, can be between 15 and 20 mm. In addition to mechanical shift of the tissue from the surgical intervention, the movements cause organ reflection in the image sequence, making optical measurements for further analysis challenging. Correcting alignment is difficult in that the motion is not uniform over the image, that is, the motion is not global over the image. Also, other artifacts, such as glare caused by illumination from surgical lights reflecting off of wet tissue surfaces (in the case of visible wavelength imaging) and evaporative cooling (in IR imaging), add random noise to the imagery, do not allow clear visualization of internal fiducial markers, and, therefore, make the task more difficult.